2D face recognition based on 3D data and MMSV features
基于三维数据与MMSV特征的二维人脸识别*

With regard to the problem of 2D face recognition is sensitive to pose and illumination variations, this paper proposed a novel approach, which was based on 3D data and MMSV features. In the training stage, used 3D face data and illumination model to generate a large number of 2D virtual images with varying pose and illumination in order to set up complete features template, and these virtual images were grouped into different subsets subsequently so as to relieve the nonlinear problems in feature extraction of human face. It extracted MMSV features at last to fuse global and local features. Recognition was accomplished by calculating the distance of MMSV feature subspace. The experiment results confirm that MMSV features contain more identifying information and the robust to pose and illumination variations. This approach achieved 98.4% recognition rate in WHU-3D database.